CN1529291A - Forest fire intelligent moritoring and alarming device - Google Patents
Forest fire intelligent moritoring and alarming device Download PDFInfo
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- CN1529291A CN1529291A CNA2003101117936A CN200310111793A CN1529291A CN 1529291 A CN1529291 A CN 1529291A CN A2003101117936 A CNA2003101117936 A CN A2003101117936A CN 200310111793 A CN200310111793 A CN 200310111793A CN 1529291 A CN1529291 A CN 1529291A
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- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B17/00—Fire alarms; Alarms responsive to explosion
- G08B17/005—Fire alarms; Alarms responsive to explosion for forest fires, e.g. detecting fires spread over a large or outdoors area
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Abstract
The device comprises: fire signal detector for detecting fire signal in site; signal transfer device for transferring fire signal to fire database and management device, which process data; then, sending varied procedure of signal of physical quantity through signal transfer device to fire alarming controller; comparing the said varied procedure with reference mode, the fire alarming controller makes decision of whether fire happens as well as based on the said decision, determines whether fire alarming signal is needed to send out; fire alarming signal through A/D converter is transferred to fire alarming device, which sends out alarming signal. Computer system, fuzzy theory and technique of artificial neural network are utilized to assistant intelligent device of monitoring forest fire to carry out processing in real tine. The device possessing features of fast reaction speed and high fault tolerant is capable of monitoring and forecasting forest fire with accuracy and in time.
Description
Technical field
The present invention relates to a kind of forest fire monitoring warning device, more particularly, be a kind of forest fires model of setting up based on chaology research institute, combine to have the forest fire intellectual monitoring warning device of self-learning function with fuzzy theory and artificial neural network technology.
Background technology
Traditional fire alarm installation, its formation is: the physical signalling such as temperature, concentration, luminosity, tone that is detected relevant fire by sensing element; Through intermediate transfer, amplifier element, physical signalling is converted into electric signal; Again electric signal is sent to zone or console for centralized control, and provide instruction, start extinguishing device.This method is based on fire and shows as combustion phenomena out of control on the space-time, can produce flame, noise, products of combustion and a large amount of heat during burning.Thisly send fire alarm automatically, start the pull station of extinguishing device automatically, the past is often because it has certain automaticity, and is considered to intelligent fire alarm.In fact, intelligent fire alarm key issue is that determination methods and foundation that whether the control station decision sends fire alarm are not mechanical comparisons, but have the judgement of artificial intelligence.If according to a certain threshold value of setting (constant type) or the comparison of threshold region (float type), so, this threshold method remains mechanical, and it is by setting (or a plurality of) judgment threshold, in case the fire physical signalling of being surveyed surpasses this threshold value, just sends fire alarm.China Patent No. is CN1348156A, and patent name is provided with sensor for " automatic fire alarm and fire alarm artificial intelligence dispatching system " in the place of needs monitoring, and the signal of sensor is passed to supervisory system through single-chip microcomputer.Said system mainly uses sensor to transmit signal, through intermediate transfer, amplifier element, physical signalling is converted into electric signal; Send electric signal to supervisory system again, supervisory system outputs signal to the firehouse.Obviously, this is an a kind of mechanical threshold method system.Burn and be not equal to fire in fact.For example, forest part, somewhere is heated suddenly, or has many people to smoke in a little space, produces a large amount of smog, and this generally is can not form fire, and at this moment threshold method may produce false alarm.In a single day for another example, Yin Senlin's glows, and may experience in short-term less than heat, cigarette, but experience, then formed unmanageable fire, and at this moment threshold method then may produce and fail to report the police.Therefore,, depend on the sensitivity of sensor to a great extent, and the interference of other signal that is subject to occur once in a while, report by mistake, to fail to report alarm rate higher although the threshold method warning device is comparatively easy.
Summary of the invention
The objective of the invention is to overcome existing fire alarm installation theoretical foundation imperfection, intelligent degree is not high, wrong report, the shortcoming that rate of failing to report is higher, a kind of novel forest fire intellectual monitoring warning device is provided, its transmission be the change procedure of fire signal, rather than traditional threshold method, the forest fire reference pattern contrast that this signal change procedure and the present invention are set up, make the judgement of fire alarm, the fire the condition of a disaster that to install each time simultaneously to be made is judged and the fire alarm accuracy rate feeds back to the forest fire database, artificial neural network technology carries out self study with feedback data, makes device have self-learning function; Improve the accuracy and the promptness of forest fire prognosis and prediction fire alarm.
Forest fire intellectual monitoring warning device of the present invention comprises:
Fire signal detector is used for on-the-spot detection of fires signal;
Apparatus for transmitting signal is used for that fire signal detector is detected fire signal and is input to fire data base and management devices;
Fire data base and management devices carry out typing, processing and management to data, and the change procedure with physical quantity signal sends fire alarm control unit to through apparatus for transmitting signal again;
Fire alarm control unit, sort signal changed course and forest fire database and management devices store in advance, the reference pattern that is obtained by model experiment, numerical evaluation, artificial neural network technology self study or Field Research compares mutually, make the judgement whether fire takes place, whether provide the alarm of fire signal according to this judgement decision again;
Fire alarm device, the alarm of fire signal is passed to Fire alarm device through digital to analog converter and is given the alarm;
Described forest fire database and management devices are computing machines.
Also comprise:
Manual fire alarm call point, being used for makes a response to fire alarm control unit artificially implements alarm effectively.
The fire the condition of a disaster that described Fire alarm device is made each time judges that record and fire alarm accuracy rate feed back to forest fire database and management devices through analog to digital converter, through artificial neural network technology feedback data is carried out self study, constantly revise sample set and decision rule.
By two bus principles all fire signal detector are connected, bus is received analog to digital converter, through analog to digital converter signal is imported forest fire database and management devices, signal input fire alarm control unit after operation is handled, carry out the contrast of forest fire pattern, make fire alarm and judge that signal passes to Fire alarm device through digital to analog converter and sends the alarm of fire signal.
Alarm device is connected with forest fire database and management devices through analog to digital converter, and the fire the condition of a disaster that fire alarm control unit is each time made judges and the fire alarm accuracy rate is returned the forest fire database through the artificial and automatic Control and Feedback of analog to digital converter.
Described forest fire signal sensor comprises fume detector, tropical vegetation pattern detector, hygrosensor, meteorological factor detector, topographic condition detector, and wherein one or more fixtures use simultaneously.
Described forest fire database and management devices are realized by computing machine, database storing has in a large number about data and forest fire models such as tropical vegetation pattern, meteorological factor, topographic condition, fire occurrence record, Tropical forests combustible composition, pyrolysis characteristics, fire behaviour, combustion characteristics, this data bank management device has the Tropical forests fire reference pattern based on fuzzy theory and artificial neural network training, has the function of inquiry, data analysis, Knowledge Discovery simultaneously.
What described modulus and digital to analog converter transmitted is detector physical quantity signal change procedure, rather than discrete signal, judges making fire after its physical quantity signal change procedure and the contrast of forest fire reference pattern.
Described forest fire model algorithm comprises based on nonlinear Combustible Materials model, banging of forest fuel based on mutationism fired model and forest fire appealing model, based on the fire smoke motion turbulence model of chaos and coupling map grid, based on forest fire appealing, the progressions model of insect population equation.
Described forest fire reference pattern of training based on fuzzy theory and artificial neural network is to obtain sample set about forest fires by the forest fire database, and carry out simulated training by artificial neural network technology and obtain, and, fire decision rule and reference pattern have been determined in conjunction with fuzzy theory.
The function of described data analysis, Knowledge Discovery has the process of Knowledge Discovery, the fire the condition of a disaster that fire alarm control unit is each time made judges and the fire alarm accuracy rate feeds back to the forest fire database through analog to digital converter, artificial neural network technology carries out feedback data self study and data is carried out Knowledge Discovery (KDD), constantly revise sample set and decision rule, and then improve the forest fire reference pattern; The deposit data that feeds back at last is in fire data base, as the differentiation data basis of later forest fire.
Described fire alarm control unit is intelligent alarm.
The present invention with the advantage that existing method and technology is compared is:
(1) introduces nonlinear kinetics forest fire mechanism is carried out the resulting forest fires model of fundamental research, complicacy and uncertainty to fire have had quantitative calculation, monitoring, alarming to forest fire has strong theoretical foundation, has strengthened the reliability and the science of forest fire sample set greatly;
(2) utilization artificial neural network technology, the forest fire model of energy Simulation of Complex, the Nonlinear Mapping of output bounded to constitute the nonlinear model of describing forest fire, has the height fault-tolerance;
(3) Benson forest fires calamity intellectual monitoring warning device is realized and the computer system parallel processing, and travelling speed is fast, and the reaction time is short;
(4) forest fire database and management devices and the data digging system set up of this device has self-learning capability preferably, becomes the intelligent forest monitor alarm for fire accident, improves the accuracy and the promptness of forest fire prognosis and prediction fire alarm.
Description of drawings
Fig. 1 is the structural representation of forest fire intellectual monitoring warning device of the present invention;
Fig. 2 is the principle schematic of forest fire intellectual monitoring warning device of the present invention;
Fig. 3 is the Knowledge Discovery KDD system model structural drawing of forest fire intellectual monitoring warning device shown in Figure 2;
Fig. 4 is the processing procedure synoptic diagram of Knowledge Discovery in the fire data base of forest fire intellectual monitoring warning device shown in Figure 2.
Embodiment
Below in conjunction with accompanying drawing the specific embodiment of the present invention is described in further detail, but of the present invention
Embodiment is not limited thereto.
Below in conjunction with accompanying drawing and embodiment the present invention is described in further detail:
Attached Fig. 1 and 2 has represented embodiment of the present invention.The forest fire intellectual monitoring warning device of the present invention's exploitation, the tropical vegetation pattern that the scene is surveyed and investigation obtains, meteorological factor, topographic condition, data 1 such as fire occurrence record are imported forest fire database and management devices 2 to signal through analog to digital converter, handle through forest fire database and management devices 2 operations, change procedure 3 with physical quantity signal sends fire alarm control unit 4 to through apparatus for transmitting signal again, fire alarm control unit 4 changes sort signal that course and forest fire database and management devices 2 store in advance, by model experiment, numerical evaluation, the reference pattern 7 that artificial neural network technology self study or Field Research obtain compares mutually, make the judgement whether fire takes place, whether provide the alarm of fire signal according to this judgement decision again, signal is passed to Fire alarm device through digital to analog converter and is sent the alarm of fire signal, artificially fire alarm control unit 4 is made a response by manual fire alarm call point 5 alarm is implemented effectively, the fire the condition of a disaster that alarm device 8 is made each time judges that record and fire alarm accuracy rate feed back to forest fire database and management devices 2 through analog to digital converter simultaneously, through artificial neural network technology feedback data is carried out self study, constantly revise sample set and decision rule, and then improve forest fire reference pattern 7, improve the accuracy of forest fire prognosis and prediction fire alarm.
Below forest fire database of the present invention and management devices are described further.
The forming process of forest fire database is as follows: fire be one relate to burning, heat transfer, flow, sensing, automatic control, weather, meteorology, forest multi-disciplinary cross disciplines such as (or buildings, mine, tunnel).Pyrolysis kinetics rule, volatile matter component and combustion characteristics, the fixed carbon that at first must grasp relevant combustible catches fire and burning burns characteristic, flame propagation velocity; Set up combustible Hong combustion then whether judgment models whether lasting with fire, development takes place; The diffusion of forecast combustion product, mobile, filling rule; Final structure is considered fire prediction, the forecasting model of various correlative factors.Fire process comprises: the generation of thermal source; Combustible is by convection current, radiation or heat conduction heating, Combustible Materials; Volatile matter spreads mixing in air; Volatile matter Hong combustion; Fixed carbon catches fire, burns, burns, and thermal convection produces, development, smoke movement; Fire spread, extensive fire forms, development.The complicacy of fire is that it is one and is subjected to multiple factor affecting, the turbulent flow of band chemical reaction, and turbulent flow itself is exactly a strong nonlinear process, is difficult to method with Classical Fluid Mechanics and obtains it and determine to separate; The diffusion of pyrolysis simultaneously, products of combustion, flow and combustion process in many features and the special fire behavior during fire spread, have very strong nonlinear characteristic.The non-linear complexity behavior of forest fire is studied and described to the utilization non-linear dynamic model, through to the generation of fire, spread the further investigation of development mechanism, as the basis of the required reliable sample set of artificial neural network training.The inventor to following forest fires Study of model as Ben Fa and the bright theoretical research basis of neural network prediction thereof: based on the forest fire energy variation law-analysing of mass-energy conservation; Forest fire appealing characteristic research based on analysis of Heat Transfer; Forest fire appealing analysis based on mutationism; The nonlinear model in temperature field and neural network prediction thereof; Chaotic Property Analysis based on the flue gas plume of chaology; Hong the combustion model of Tropical forests combustible; Flue gas plume model based on coupling map grid pattern; The characteristic analysis that the Tropical forests fire produces; Forest fire appealing analysis based on insect.Through obtaining the research of carrying out the self-organized criticality behavior of fire in a large number about data such as tropical vegetation pattern, meteorological factor, topographic condition, fire occurrence record; By the branch of plant and the classification of building materials, select the some kinds of typical combustibles in the torrid zone, carry out pyrolysis, combustion experiment, derive the Combustible Materials reaction dynamics law.Set up the prediction of flashover model, derive the thermal condition of Hong combustion.By experiment to representative condition and object, deep combustion mechanism analysis, reach mathematical physics simulation and numerical simulation, disclose and describe forest fire generation, characteristic of development and rule based on Cusp Catastrophe Model, Logistic difference model, coupling map grid pattern.
The generative process of forest fire reference pattern 7 of the present invention is as follows:
Fire phenomena has polytrope.In fire formation, the evolution, each physical quantity shows certain rules on the whole, and the instantaneous value of a certain physical quantity or a plurality of physical quantitys often shows certain randomness, has uncertainty.Generation and development that the humidity of the generation of the geometry gradient of Different Forest, the pyrolysis and combustion characteristic of combustible and degree of drying, thermal source and intensity, air and multiple factors such as temperature, wind-force and wind direction all have influence on fire.Like this, actual detection to signal often be difficult to and the pattern that provides fits like a glove, this just requires mode identification procedure to have stronger fuzzy analogy, identification and fault-tolerant ability.Fuzzy theory, artificial neural network have sample self-learning capability, high fault tolerance, are to realize complicated mode classification and the effective tool of debating knowledge.The people is when understanding and processing things, employed logic often is not the absolute "Yes" and the two-valued function of " non-", promptly, differentiate the subordinate degree of an incident for a certain category, it or not absolute " 1 ", " 0 " that neither be absolute, and usually be the boundary between " 1 " and " 0 ", this is fuzzy logic just---the continuous feature of multi valued logic.Artificial neural network technology is at present the most outstanding in the world intelligent theory, and the function of its self-adaptation, self study can be simulated the feature of nonlinear system well.With combinations such as fuzzy logic theory, artificial neural network and above-mentioned fire theory, fire basic datas, form real intelligent fire alarm technology.Obtained sample set by detector, investigation, experiment, Theoretical Calculation about forest fires, utilize artificial neural network technology to train by this sample set, form the intellectual monitoring of the forest fire efficiently warning device that has sample self-learning capability, high fault tolerance, realization complicated mode classification and debate knowledge.
Using artificial neural networks of the present invention is trained the above forest fires model.The ANN structure that contains hidden layer has been selected in design for use, and input variable is the control variable through each model of pre-service.Selected the complicated combustible of non-linear correlation as research object according to the combustion characteristics of combustible in the design, selected the hidden layer node number by trial method.The error backpropagation algorithm (BP) after improving is adopted in the training of neural network, has adopted the speed technology of dynamic adjustment step-length.BP algorithm after the improvement in most cases can effectively be jumped out local minimum point, and the saturated of its training process also is an efficiency index that judges whether to reach the network capacity limit.Adopt said method finally to select three layers and four layers of feedforward neural network model.
So far by fundamental research, laboratory experiment, Field Research and numerical simulation to fire mechanism, accumulated the basic data of relevant fire, and obtained sample set about forest fires by detector, investigation, experiment, Theoretical Calculation, train by this sample set using artificial neural networks technology, and set up forest fire database and management devices 2; Extract the fire signal eigenwert, constitute decision rule and reference pattern 7 that the artificial intelligence training obtains; The inventor has drawn the reliable monitoring, alarming effect of expection according to actual experiment.
Below in conjunction with Fig. 3 and Fig. 4 forest fire Knowledge Discovery in Database process of the present invention is described further.
Accompanying drawing 3 is the structural drawing of Knowledge Discovery KDD system, the inventor will study the relevant data of fire gained and set up fire data base DBF, fire data base DBF is handled at data base management system (DBMS) DBMS, make fire data base carry out man-machine conversation with user interface, user interface is sent instruction by computing machine and is given KDD module and DM module, and data base management system (DBMS) DBMS and KDD module and DM module are carried out the Knowledge Discovery process mutually.The inventor uses the principle of accompanying drawing 4 that the relevant data of fire is carried out Knowledge Discovery, obtains the high decision rule of reliable fault-tolerance.From fire data base, filter out the target data of judging that whether fire takes place on the one hand by user interface, determine the KDD target on the other hand, decide the KDD system algorithm.Decide the KDD system algorithm fire target data are carried out data mining, mining data is carried out interpretation of scheme and knowledge evaluation, the knowledge that obtains finding, carry out collecting new data again after the knowledge application, the data of newly collecting are fed back to fire data base, fire data base is replenished and perfect.
In sum, the performing step of forest fire monitoring warning device of the present invention is as follows: with the vegetation pattern that the detector scene is surveyed and investigation obtains, meteorological factor, topographic condition, data 1 such as fire occurrence record are imported forest fire database and management devices 2 to signal through analog to digital converter, send physical quantity signal change procedure 3 to fire alarm control unit 4 through apparatus for transmitting signal again, physical quantity signal change procedure 3 compares with the forest fire reference pattern 7 that system of the present invention sets up, obtain prediction case after the contrast to the forest fire the condition of a disaster, make the judgement whether forest fire takes place, whether provide fire alarm signal according to this judgement decision again, judging has after the fire alarm, fire alarm is passed to warning system 8 through digital to analog converter, 9,10,11,12, fire department is received the work of carrying out the forest fires of putting out a fire to save life and property after the fire alarm.Artificially fire alarm control unit 4 is made a response by manual fire alarm call point 5 alarm is implemented effectively, judgement 8 of fire the condition of a disaster and fire alarm accuracy rate that device is made each time feed back to forest fire database and management devices 2 through analog to digital converter, artificial neural network technology carries out self study with feedback data, constantly revise sample set and decision rule, and then improve forest fire reference pattern 7; The deposit data that feeds back at last is in fire data base 2, as the basic data of later forest fire differentiation.The forest fire database that the present invention uses the non-linear mechanism of fire, fuzzy theory and artificial neural network technology to combine and set up, have the ability of self study and the process of Knowledge Discovery, obtain high training sample set of reliable fault-tolerance and decision rule, constantly the oneself replenishes and is perfect, help the mechanism research of forest fires and improve the intelligentized degree of fire alarm, improve accuracy, promptness and the reliability of forest fire fire alarm greatly.
Claims (3)
1, a kind of forest fire intellectual monitoring warning device comprises:
Fire signal detector is used for on-the-spot detection of fires signal;
Apparatus for transmitting signal is used for that fire signal detector is detected fire signal and is input to fire data base and management devices;
Fire data base and management devices carry out typing, processing and management to data, and the change procedure with physical quantity signal sends fire alarm control unit to through apparatus for transmitting signal again;
Fire alarm control unit, sort signal changed course and forest fire database and management devices store in advance, the reference pattern that is obtained by model experiment, numerical evaluation, artificial neural network technology self study or Field Research compares mutually, make the judgement whether fire takes place, whether provide the alarm of fire signal according to this judgement decision again;
Fire alarm device, the alarm of fire signal is passed to Fire alarm device through digital to analog converter and is given the alarm;
Described forest fire database and management devices are computing machines.
2, forest fire intellectual monitoring warning device according to claim 1 is characterized in that also comprising manual fire alarm call point, and being used for makes a response to fire alarm control unit artificially implements alarm effectively.
3, forest fire intellectual monitoring warning device according to claim 1 and 2, it is characterized in that fire the condition of a disaster judgement record and fire alarm accuracy rate that described Fire alarm device alarm device is made each time feed back to forest fire database and management devices through analog to digital converter, through artificial neural network technology feedback data is carried out self study, constantly revise sample set and decision rule.
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101251942B (en) * | 2008-03-14 | 2010-04-21 | 华南理工大学 | Underground space fire intelligent detection early alarming and forecasting method and apparatus |
| CN1873418B (en) * | 2006-05-31 | 2011-01-19 | 华南理工大学 | Layered liquified natural gas, and alarm device for detecting vortex |
| CN102609821A (en) * | 2012-02-21 | 2012-07-25 | 丰国炳 | Portable safety management system for fire scene interior, and implementation method thereof |
| CN102663082A (en) * | 2012-04-06 | 2012-09-12 | 昆明理工大学 | Forest fire forecasting method based on data mining |
| CN103369004A (en) * | 2012-03-30 | 2013-10-23 | 罗宁 | Method for carrying out security monitoring and resource management on building by using human sensors |
| CN103514700A (en) * | 2013-09-29 | 2014-01-15 | 柳州市宏亿科技有限公司 | Method for designing forest fire prevention early warning system |
| CN103885471A (en) * | 2014-02-20 | 2014-06-25 | 中国林业科学研究院森林生态环境与保护研究所 | Forest combustible matter humidity automatic regulating system and method based on forest fire hazard |
| CN104933841A (en) * | 2015-04-30 | 2015-09-23 | 重庆三峡学院 | Fire prediction method based on self-organizing neural network |
| WO2019200743A1 (en) * | 2018-04-17 | 2019-10-24 | 平安科技（深圳）有限公司 | Forest fire prediction method, apparatus, computer device, and storage medium |
-
2003
- 2003-10-16 CN CNA2003101117936A patent/CN1529291A/en active Pending
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1873418B (en) * | 2006-05-31 | 2011-01-19 | 华南理工大学 | Layered liquified natural gas, and alarm device for detecting vortex |
| CN101251942B (en) * | 2008-03-14 | 2010-04-21 | 华南理工大学 | Underground space fire intelligent detection early alarming and forecasting method and apparatus |
| CN102609821A (en) * | 2012-02-21 | 2012-07-25 | 丰国炳 | Portable safety management system for fire scene interior, and implementation method thereof |
| CN103369004A (en) * | 2012-03-30 | 2013-10-23 | 罗宁 | Method for carrying out security monitoring and resource management on building by using human sensors |
| CN102663082A (en) * | 2012-04-06 | 2012-09-12 | 昆明理工大学 | Forest fire forecasting method based on data mining |
| CN103514700A (en) * | 2013-09-29 | 2014-01-15 | 柳州市宏亿科技有限公司 | Method for designing forest fire prevention early warning system |
| CN103885471A (en) * | 2014-02-20 | 2014-06-25 | 中国林业科学研究院森林生态环境与保护研究所 | Forest combustible matter humidity automatic regulating system and method based on forest fire hazard |
| CN104933841A (en) * | 2015-04-30 | 2015-09-23 | 重庆三峡学院 | Fire prediction method based on self-organizing neural network |
| WO2019200743A1 (en) * | 2018-04-17 | 2019-10-24 | 平安科技（深圳）有限公司 | Forest fire prediction method, apparatus, computer device, and storage medium |
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